Search Results (27)

The sustainable utilization of saline water resources represents an effective strategy for alleviating water scarcity in arid regions. However, the mechanisms by which prolonged saline water irrigation influences soil salinization and dryland crop growth are not yet fully understood. This study examined the effects of six irrigation water salinity levels (CK: 0.87 g·L⁻¹, S1: 2 g·L⁻¹, S2: 4 g·L⁻¹, S3: 6 g·L⁻¹, S4: 8 g·L⁻¹, S5: 10 g·L⁻¹) on soil salinization dynamics and jujube growth during a three-year field experiment (2020–2022). The results showed that soil salinity within the 0–1 m profile significantly increased with rising irrigation water salinity and prolonged irrigation duration, with the 0–0.4 m layer accounting for 50.27–74.95% of the total salt accumulation. A distinct unimodal salt distribution was observed in the 0.3–0.6 m soil zone, with the salinity peak shifting downward from 0.4 to 0.5 m over time. Meanwhile, soil pH and sodium adsorption ratio (SAR) increased steadily over the study period. The dominant hydrochemical type shifted from SO₄²⁻-Ca²⁺·Mg²⁺ to Cl⁻-Na⁺·Mg²⁺. Crop performance exhibited a nonlinear response to irrigation salinity levels. Low salinity (2 g·L⁻¹) significantly enhanced plant height, stem diameter, leaf area index (LAI), vitamin C content, and yield, with improvements of up to 12.11%, 3.96%, 16.67%, 16.24%, and 16.52% in the early years. However, prolonged exposure to saline irrigation led to significant declines in both plant growth and water productivity (WP) by 2022. Under high-salinity conditions (S5), yield decreased by 16.75%, while WP declined by more than 30%. To comprehensively evaluate the trade-off between economic effects and soil environment, the entropy weight TOPSIS method was employed to identify S1 as the optimal irrigation treatment for the 2020–2021 period and control (CK) as the optimal treatment for 2022. Through fitting analysis, the optimal irrigation water salinity levels over 3 years were determined to be 2.75 g·L⁻¹, 2.49 g·L⁻¹, and 0.87 g·L⁻¹, respectively. These findings suggest that short-term irrigation of jujube trees with saline water at concentrations ≤ 3 g·L⁻¹ is agronomically feasible. Full article

This study focuses on assessing the hydrogeochemical characteristics and irrigation suitability of groundwater in the Ras El Aioun and Merouana districts, using an integrated approach that combines physicochemical analysis, machine learning (ML), and Geographic Information Systems (GISs). Thirty groundwater samples were collected in June 2023 and subjected to extensive analyses, including major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃⁻, Cl⁻, SO₄²⁻), pH, TDS, alkalinity, and hardness. Hydrochemical facies analysis revealed that the Ca-HCO₃ type was dominant (93.33%), with some samples exceeding FAO limits, particularly for Na⁺, K⁺, SO₄²⁻, Cl⁻, Mg²⁺, and HCO₃⁻. Assessment of groundwater irrigation suitability revealed generally favorable conditions based on three key parameters: all samples (100%) were classified as excellent based on the Sodium Adsorption Ratio (SAR < 10), 70% showed good-to-permissible status by Sodium Percentage (Na% < 60), and 83.3% were within safe limits for Residual Sodium Carbonate (RSC < 1.25 meq/L). However, the Permeability Index (PI > 75%) categorized 96.7% of samples as unsuitable for long-term irrigation due to potential soil permeability reduction. Additionally, Total Hardness (TH < 75 mg/L) indicated predominantly soft water characteristics (90% of samples), particularly in the central study area, suggesting possible limitations for certain agricultural applications that require mineral-rich water. GIS-based spatial analysis showed that irrigation suitability was higher in the eastern and western regions than in the central zone. Advanced machine learning algorithms provide superior predictive capability for water quality parameters by effectively modeling complex, non-linear feature interactions that conventional statistical approaches frequently fail to capture. Three ML models—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were used to predict the Irrigation Water Quality Index (IWQI). XGBoost outperformed the others (RMSE = 2.83, R² = 0.957), followed by RF (RMSE = 3.12, R² = 0.93) and SVR (RMSE = 3.45, R² = 0.92). Integrating ML and GIS improved groundwater quality assessment and provided a robust framework for sustainable irrigation management. These findings provide critical insights for optimizing agricultural water use in water-scarce regions. Full article

Aquaponics is an integrated method of aquatic animal and plant cultivation in a closed recycling system where the wastewater from aquatic animals is purified by microbes, which transform pollutants into nutrients for plants at the end of the chain. This technology allows to the efficiency of the area to be increased by a combination of cultivated plants and aquatic animals. Aquaponics produces environmentally friendly products by reducing fertilizer use and wastewater volume, increasing the extent of reuse by up to >90%. A promising way to increase efficiency in aquaponics is to use bacterial preparations (probiotics). This will allow control of the development of pathogens in the growing system, improving water quality and the growth rate of aquatic organisms. This paper overviews the experience of using probiotic preparations in aquaponic systems. It is shown that probiotics are able to increase the survival rate of aquatic organisms, improve the hydrochemical regime in recirculating aquaculture systems, and mitigate the risk of pathogenic contamination. There are a number of problems in aquaponics that prevent it from becoming more widespread and achieving maximum productivity, including problems with optimal pH and temperature, problems with nutrient and oxygen depletion, as well as diseases caused by phytopathogens and fish pathogens. The probiotics used do not take into account the biological needs of all components of the aquaponic system. The development of probiotic preparations from soil bacteria of the genus Bacillus will allow us to create a new class of probiotics specifically for aquaponics. Such preparations will work in a wide pH range, which will allow us to achieve maximum productivity for all components of aquaponics: animals, plants and bacteria. Full article

The salinization of cultivated soil in arid zones is a core obstacle restricting the sustainable development of agriculture, particularly in regions like Xinjiang, China, where extreme aridity and intensive irrigation practices exacerbate salt accumulation through evaporation–crystallization cycles. Conventional drip irrigation, while temporarily mitigating surface salinity, often leads to secondary salinization due to elevated water tables and inefficient leaching. Recent studies highlight the potential of integrating drip irrigation with subsurface drainage systems to address these challenges, yet the synergistic mechanisms governing ion transport dynamics, hydrochemical thresholds, and their interaction with crop physiology remain poorly understood. In this study, we analyzed the effects of spring irrigation during the non-fertile period, soil hydrochemistry variations, and salt ion dynamics across three arid agroecosystems in Xinjiang. By coupling drip irrigation with optimized subsurface drainage configurations (burial depths: 1.4–1.6 m; lateral spacing: 20–40 m), we reveal a layer-domain differentiation in salt migration, Cl⁻ and Na⁺ were leached to 40–60 cm depths, while SO₄²⁻ formed a “stagnant salt layer” at 20–40 cm due to soil colloid adsorption. Post-irrigation hydrochemical shifts included a 40% decline in conductivity, emphasizing the risk of adsorbed ion retention. Subsurface drainage systems suppressed capillary-driven salinity resurgence, maintaining salinity at 8–12 g·kg⁻¹ in root zones during critical growth stages. This study establishes a “surface suppression–middle blocking–deep leaching” three-dimensional salinity control model, providing actionable insights for mitigating secondary salinization in arid agroecosystems. Full article

Western Kazakhstan is susceptible to desertification, with surface water resource scarcity constraining agricultural development. Groundwater has substantial potential as a reliable and secure alternative to other water resources, particularly for irrigation, which is required to ensure food security. Eight aquifer segments with an exploitable potential of 0.24 km³/year have been identified for the integrated assessment of groundwater’s suitability for irrigation. The assessment criteria included hydro-chemical groundwater characteristics and irrigated land soil-reclamation conditions. The primary objectives of this study were to assess the groundwater quality for irrigation and to develop a practical operation scheme for rational groundwater use in water-saving irrigation technologies and optimize agricultural crop cultivation. Approximately 90% of the groundwater in these aquifer segments was found to be suitable for irrigation, with a total amount of 6520 thousand m³/day and a salinity of up to 1 g/L, and an additional 12,971 thousand m³/day had a water salinity of up to 3 g/L. Only approximately 10% had TDS values above 3 g/L and up to 6.5 g/L, categorized as conditionally suitable for restricted customized agricultural crop irrigation. Irrigated land development by complex soil desalination agro-reclamation operations enabled the use of brackish water for irrigation. The integrated analysis allowed the development of drip irrigation and sprinkling system irrigation schemes that gradually replaced wasteful surface irrigation. The irrigated land prospective area recommended for groundwater irrigation development is 653 km², with the further restructuring of cultivated areas, reducing the number of annual grasses and grain crops and increasing the number of vegetables, potatoes, and perennial grasses. Full article

Coal mine safety is vital not only for maintaining production operations but also for ensuring the industry’s sustainable development. The threat posed by mine water hazards is especially severe, growing more critical as mining activities become more intense and reach greater depths. Currently, common methods for identifying water sources mainly depend on hydrochemical data, supplemented by analyses of water level and temperature changes. However, due to constraints in cost, time, and the complexity of mining conditions, there is still significant potential for enhancing water source identification techniques. To advance water source identification, this study introduces a novel approach that uses a spectrophotometer to gather spectral data from water sources. These data are then integrated with a bat algorithm (BA)-optimized radial basis function (RBF) neural network to develop a model for identifying water inrush sources. At Baode Coal Mine in China, 105 water samples from four different sources were collected and analyzed using spectral data. The baseline was corrected using the second derivative technique to ensure the data’s integrity. Additionally, 54 sets of historical hydrochemical data were collected for comparison with the spectral data-based model. Theoretical analysis and experimental results show that both hydrochemical and spectral data are effective for identifying water inrush sources. The hydrochemical data model achieved an accuracy of about 90%, whereas the model based on spectral data reached an average accuracy of 95%. Among the tested models: RBF, GA-RBF, PSO-RBF, BA-RBF, and the BA-RBF model demonstrated superior performance, providing the most rapid and accurate identification of water inrush. Full article

Presently, geothermal resources have been globally recognized as an indispensable component of the energy system due to their sustainability. However, previous studies on geothermal reservoirs focus primarily on single reservoirs, lacking a systematic investigation of composite geothermal reservoirs. The geothermal reservoirs in the northwestern Shandong geothermal area in China are primarily of sandstone and karst types, characterized by extensive distributions, shallow burial depths, high water temperatures, and high water abundance, holding considerable potential for exploitation. This study explored the hydrochemical, isotopic, and circulation characteristics of geothermal fluids in the composite geothermal reservoirs in the study area using methods like hydrogeochemistry and geothermal geology. The purpose is to determine the geochemical differences in geothermal fluids across the composite geothermal reservoirs and provide scientific support for subsequently efficient and sustainable exploitation and utilization of geothermal resources in the study area. The composite geothermal reservoirs in the study area are composed of porous sandstone geothermal reservoirs (also referred to as sandstone reservoirs) in the upper part and karst-fissured geothermal reservoirs (also referred to as karst reservoirs) in the lower part. The results show that the geothermal fluids in the sandstone and karst reservoirs are primarily of Na-Cl-SO₄ and Na-Ca-Cl-SO₄ types, respectively. The hydrochemical composition of geothermal fluids in the karst reservoirs is principally influenced by the precipitation–dissolution equilibrium of carbonate and sulfate minerals, while that in the sandstone reservoirs is predominantly influenced by the precipitation–dissolution equilibrium of carbonate and silicate minerals, as well as cation exchange reactions. The temperatures of the karst reservoirs were calculated at 52.9–82.09 °C using geothermometers. Given the cold-water mixing ratios range from 89% to 96%, the corrected reservoir temperatures vary from 200 to 225 °C. In contrast, the temperatures of the sandstone reservoirs were calculated at 60.54–85.88 °C using geothermometers. These reservoirs exhibit cold water mixing ratios ranging from 85% to 90%, and their corrected reservoir temperatures vary from 150 to 200 °C accordingly. The circulation depths of geothermal fluids in the karst and sandstone reservoirs range from 1107.28 to 1836.69 m and from 1366.60 to 2102.29 m, respectively. The study area is primarily recharged by meteoric water from Mount Tai and the Lushan and Yishan mountains (collectively referred to as the Tai-Lu-Yi mountains) to the southeast of the study area. Investigating the differences in geochemical characteristics of geothermal fluids in composite geothermal reservoirs in the study area is significant for balancing the exploitation and supply of geothermal resources, optimizing the exploitation and utilization modes, and promoting the efficient and sustainable exploitation and utilization of geothermal resources in the study area. Full article

Access to clean water is a fundamental human need, yet millions of people worldwide still lack access to safe drinking water. Traditional water quality assessments, though reliable, are typically time-consuming and resource-intensive. This study investigates the application of machine learning (ML) techniques for analyzing river water quality in the Barnaul area, located on the Ob River in the Altai Krai. The research particularly highlights the use of the Water Quality Index (WQI) as a key factor in feature engineering. WQI, calculated using the Horton model, integrates nine hydrochemical parameters: pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The primary objective was to demonstrate the contribution of WQI in enhancing predictive performance for water quality analysis. A dataset of 2465 records was analyzed, with missing values for parameters (pH, sulfate, and trihalomethanes) addressed using predictive imputation via neural network (NN) architectures optimized with genetic algorithms (GAs). Models trained without WQI achieved moderate predictive accuracy, but incorporating WQI as a feature dramatically improved performance across all tasks. For the trihalomethanes model, the $R^2$ score increased from 0.68 (without WQI) to 0.86 (with WQI). Similarly, for pH, the $R^2$ improved from 0.35 to 0.74, and for sulfate, from 0.27 to 0.69 after including WQI in the feature set. Full article

This study explores the effects of varying exposure times of microelement fertilization on hydrochemical parameters, plant growth, and nutrient content in an aquaponic system cultivating Capsicum annuum L. (pepper) with Cyprinus carpio (Common carp L.). It also investigates the potential of visible–near-infrared (VIS-NIR) spectroscopy to differentiate between treated plants based on their spectral characteristics. The findings aim to enhance the understanding of microelement dynamics in aquaponics and optimize the use of VIS-NIR spectroscopy for nutrient and stress detection in crops. The effects of microelement exposure on the growth and health of Cyprinus carpio (Common carp L.) in an aquaponic system are investigated, demonstrating a 100% survival rate and optimal growth performance. The findings suggest that microelement treatments, when applied within safe limits, can enhance system productivity without compromising fish health. Concerning hydrochemical parameters, conductivity remained stable, with values ranging from 271.66 to 297.66 μS/cm, while pH and dissolved oxygen levels were within optimal ranges for aquaponic systems. Ammonia nitrogen levels decreased significantly in treated variants, suggesting improved water quality, while nitrate and orthophosphate reductions indicated an enhanced plant nutrient uptake. The findings underscore the importance of managing water chemistry to maintain a balanced and productive aquaponic system. The increase in root length observed in treatments 2 and 6 suggests that certain microelement exposure times may enhance root development, with treatment 6 showing the longest roots (58.33 cm). Despite this, treatment 2 had a lower biomass (61.2 g), indicating that root growth did not necessarily translate into increased plant weight, possibly due to energy being directed towards root development over fruit production. In contrast, treatment 6 showed both the greatest root length and the highest weight (133.4 g), suggesting a positive correlation between root development and fruit biomass. Yield data revealed that treatment 4 produced the highest yield (0.144 g), suggesting an optimal exposure time before nutrient imbalances negatively impact growth. These results highlight the complexity of microelement exposure in aquaponic systems, emphasizing the importance of fine-tuning exposure times to balance root growth, biomass, and yield for optimal plant development. The spectral characteristics of the visible–near-infrared region of pepper plants treated with microelements revealed subtle differences, particularly in the green (534–555 nm) and red edge (680–750 nm) regions. SIMCA models successfully classified control and treated plants with a misclassification rate of only 1.6%, highlighting the effectiveness of the spectral data for plant differentiation. Key wavelengths for distinguishing plant classes were 468 nm, 537 nm, 687 nm, 728 nm, and 969 nm, which were closely related to plant pigment content and nutrient status. These findings suggest that spectral analysis can be a valuable tool for the non-destructive assessment of plant health and nutrient status. Full article

Groundwater resources play a critical role in meeting the agricultural, industrial, and domestic water demands of Tangshan, a key industrial city in China. However, with the acceleration of urbanization and the overextraction of groundwater, issues related to groundwater quality have become increasingly apparent. Notably, groundwater hardness has steadily increased over the years, posing risks to human health and elevating industrial water treatment costs. This study analyzed the spatial distribution characteristics and causes of groundwater hardness using 214 groundwater quality samples collected in 2022 from the plain area of Tangshan City, employing inverse distance weighting (IDW), Gibbs diagrams, ion ratios, mineral saturation indices, and Pearson correlation analysis. The results indicate that, in horizontal distribution, high-hardness groundwater is predominantly concentrated in the southern coastal plain area, with hardness gradually decreasing from south to north. Vertically, shallow groundwater in the coastal plain exhibits significantly higher hardness than deep groundwater, with a non-compliance rate of 94.12%, while deep groundwater hardness remains markedly lower. Mid-depth groundwater (60–300 m) in the alluvial plain exhibits elevated hardness, primarily attributed to mineral dissolution and agricultural irrigation return flow. The spatial distribution pattern of groundwater hardness across the study area is predominantly governed by hydrogeochemical processes and hydrochemical environmental factors, with cation exchange adsorption and evaporation–concentration processes identified as the dominant influences. The analysis of ion sources indicates that Ca²⁺ and Mg²⁺, the primary contributors to groundwater hardness in the area, are mainly derived from the weathering and dissolution of carbonate minerals, sulfate minerals, and cation exchange processes. Therefore, an in-depth investigation into the spatial distribution and driving factors of groundwater hardness in Tangshan can provide a scientific basis for regional water resource management, pollution control, and water quality optimization. Such research also supports the development of sustainable groundwater management and optimization strategies. Full article

A comprehensive hydrogeological, geophysical, and hydrochemical investigation was conducted in southeastern Hitchcock County, Nebraska, within the Driftwood Creek alluvial aquifer. This study assessed groundwater contamination stemming from the surface disposal of saline wastes from oilfield activities. A contaminated area, initially identified through regional groundwater sampling, was examined in detail. Monitoring wells were installed, and groundwater and soil samples were collected for chemical analysis. Surface electrical resistivity surveys were also performed to delineate contamination patterns. The findings revealed that the groundwater contamination originated from the leaching of residual evaporative salts through the vadose zone, beneath an abandoned emergency-evaporation brine storage pit. Data from down-hole specific conductance logs, water quality analyses, and computer-generated interpretations of surface electrical resistivity indicated that contaminant migration was primarily influenced by gravity, bedrock topography, and the local hydraulic gradient. An initial surface electrical resistivity profile survey was conducted to optimize the placement of monitoring wells and soil sampling sites within the vadose zone. Following well installation, a contaminant source with complex brine contamination patterns was detected within the shallow aquifer. Vertical electrical soundings were then carried out as the final investigative step. The data from these soundings, combined with test hole records, water level measurements, brine contaminant distribution, and soil analyses, were refined through a computer program employing the method of steepest descent. By incorporating known layer thicknesses and resistivities as constraints, this approach minimized the common issue of non-unique electrical sounding interpretations, providing information on the distribution of brine contaminants within the alluvial aquifer. Full article

This study presents a case focused on sustainable farming practices, specifically the cultivation of tilapia (Mozambican and aureus species) in ponds with geothermal water. This research aims to optimize the hydrochemical regime of experimental ponds to enhance the growth metrics and external characteristics of tilapia breeders. The dataset encompasses the hydrochemical parameters and the fish feeding base from experimental geothermal ponds where tilapia were cultivated. Genetic algorithms (GA) were employed for hyperparameter optimization (HPO) of deep neural networks (DNN) to enhance the prediction of fish productivity in each pond under varying conditions, achieving an $R^2$ score of 0.94. This GA-driven HPO process is a robust method for optimizing aquaculture practices by accurately predicting how different pond conditions and feed bases influence the productivity of tilapia. By accurately determining these factors, the model promotes sustainable practices, improving breeding outcomes and maximizing productivity in tilapia aquaculture. This approach can also be applied to other aquaculture systems, enhancing efficiency and sustainability across various species. Full article

The cartographic landscape analysis of Lake Doshne employs geographic landscape methods, GIS cartographic modeling, and geo-ecological analysis. This study includes hydrochemical analysis of the lake’s water mass, focusing on saline blocks, tropho-saprobiological indicators, and specific toxic action indicators. Three geological sections of anthropogenic and pre-Quaternary complexes, along with a geological–lithological transverse profile of the lake basin, were developed. Additionally, a geographical landscape model of the lake’s natural aquatic complex was presented, distinguishing littoral–sublittoral and profundal aquatic sub-tracts and five types of aquafacies with landscape metric assessments. This approach enables a comprehensive analysis and the creation of cartographic models that can serve as a basis for lake cadastre and optimization of the ecological and landscape conditions in local territories. Full article

A technology for the hydrochemical processing of the kaolinite fraction of bauxite has been developed, and it involves preliminary chemical activation in a sodium bicarbonate solution and alkaline leaching in a recycled high-modulus solution with the addition of an active form of calcium oxide. Chemical activation allows for the transformation of the difficult-to-explore kaolinite phase to form easily soluble phases of dawsonite, sodium hydroaluminosilicate and bemite. An active, finely dispersed form of calcium oxide was obtained as a result of CaO quenching in Na₂SO₄ solution at elevated temperature and pressure. Using a central composite design (CCD) via response surface methodology (RSM), the technological leaching mode was achieved. The influence on the leaching process was studied by adjusting the CaO/SiO₂ ratio, temperature, alkaline solution concentration and duration. It was found that the determining factors are the concentration of the leaching solution and the temperature. At a stable CaO/SiO₂ ratio, a combination of these two factors determines the duration of the process to achieve the predicted degree of recovery. The results of experiments carried out using the developed model of the leaching process confirmed the validity of the calculated indicators, with an error of 2.01%. In an optimal technological mode at a Na₂O leaching solution concentration of 260 g/L, a temperature of 260 °C, a CaO/SiO₂ ratio of 1.5 and a leaching time of 5 h, the extraction of Al₂O₃ into the solution was 89.7%, which is close to the value of 87.9% predicted by the model. Full article

As industrial development and population growth continue, water pollution has become increasingly severe, particularly in rapidly industrializing regions like the area surrounding the Yopurga landfill. Ensuring water resource safety and environmental protection necessitates effective water quality monitoring and assessment. This paper explores the application of advanced machine learning technologies and the Water Quality Index (WQI) model as a comprehensive method for accurately assessing groundwater quality near the Yopurga landfill. The methodology involves selecting water quality indicators based on available data and the hydrochemical characteristics of the study area, comparing the performance of Decision Trees, Random Forest, and Xgboost algorithms in predicting water quality, and identifying the optimal algorithm to determine indicator weights. Indicators are scored using appropriate sub-index (SI) functions, and six different aggregation functions are compared to find the most suitable one. The study reveals that the Xgboost model surpasses Decision Trees and Random Forest models in water quality prediction. The top three indicator weights identified are pH, Manganese (Mn), and Nickel (Ni). The SWM model, with a 0% overestimation eclipsing rate and a 34% underestimation eclipsing rate, is chosen as the most appropriate WQI model for evaluating groundwater quality at the Yopurga landfill. According to the WQI results from the SWM aggregation function, the overall water quality in the area ranges from moderately polluted to slightly polluted. These assessment results provide a scientific basis for regional water environment protection. Full article